How do scientists develop and use scientific software?

By Jo Erskine Hannay, Hans Petter Langtangen, Carolyn MacLeod, Dietmar Pfahl, Janice Singer, G. Wilson. 2009

289 citations recorded by the source corpus.

Subfields: Information Systems, Information Systems and Management, Human-Computer Interaction, Social Sciences & Humanities

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Citations in the corpus

Array programming with NumPy (2020, background): “lack of formal education through courses and workshops [35, 36, 37].”
Better together: Elements of successful scientific software development in a distributed collaborative community (2020, background): “However, few training programs in these fields include instruction in computer science[6] or software engineering.”
Best Practices for Scientific Computing (2012, background): “However, 90% or more of them are primarily self-taught [1,2], and therefore lack exposure to basic software development practices such as writing maintainable code, using version control and issue trackers, code reviews, unit testing, and task automation.”
The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, unknown): “Most modelers, however, are not formally taught software development practices (Hannay et al., 2009), such that models are rarely designed and developed in a manner that supports further use (or reuse) beyond its original research-specific context.”
Influence apart from Adoption: How Interaction between Programming and Scientific Practices Shapes Modes of Inquiry in Four Oceanography Teams (2016, background): “Software engineering literature has used literature reviews [27, 58] and well as surveys and interviews [26, 34].”
From big data analysis to personalized medicine for all: challenges and opportunities (2015, background): “Approximately 90 % of scientists are self-taught in developing software and one may lack basic practices such as task automation, code review, unit testing, version control and issue tracking [42, 43].”
Collaboration gets the most out of software (2013, background): “Many of the most useful programs have been developed by other scientists (Hannay et al., 2009; Morin and Sliz, 2013), but these scientists often have little or no time to support the widespread distribution of their software and/or to provide the support needed to install it on a variety of…”
Software Engineering for Machine Learning: A Case Study (2019, background): “Similar experimental properties have been observed in the past in scientific software [15] and hardware/software co-design [16].”
Examining the usage, citation, and diffusion patterns of bibliometric mapping software: A comparative study of three tools (2018, background): “This issue is especially acute in recent years, as the variety of software vailable freely for academic use has increased tremendously (Hannay et al., 2009; Huang et al., 2013).”